Senior Staff Machine Learning Engineer,

Join to apply for the Senior Staff Machine Learning Engineer (ML Underwriting) role at Affirm Affirm is reinventing credit by making it more honest and friendly, giving consumers flexibility to buy now and pay later without hidden fees or compounding interest. As a Senior Staff Machine Learning Engineer, you will help shape the future of machine learning at the company. You will partner with ML Platform, engineering, product, and risk leaders to design, implement, and scale advanced modeling approaches that drive critical decisions across the organization. You will elevate our modeling capabilities, influence architectural direction, and ensure our systems can support increasingly sophisticated workloads. You will mentor senior engineers, bring clarity to complex, ambiguous problems, and contribute to a long‑term ML strategy. If you are passionate about modern machine learning and excited to drive high‑impact innovation across a growing organization, Affirm is the place for you. What You’ll Do Define and drive multi-year, multi-team technical strategy for machine learning across “Affirm”, ensuring alignment with company-wide priorities and influencing the roadmaps of partner teams and platforms. Lead the design, implementation, and scaling of advanced ML systems, setting the architectural direction for complex, cross‑functional initiatives and ensuring systems remain reliable, extensible, and prepared for increasingly sophisticated modeling workloads. Partner deeply with ML Platform, product, engineering, and risk leadership to shape long-term modeling capabilities, define new opportunities for ML impact, and guide infrastructure evolution required for next‑generation ML methods. Provide broad technical leadership across the ML organization, mentoring senior engineers, elevating design and code quality, and spreading ML expertise through documentation, talks, and cross‑org guidance. Drive clarity and alignment on ambiguous, high‑stakes technical decisions, resolving cross‑team tensions, balancing competing priorities, and exercising judgement optimized for the broader engineering organization. Champion operational and system excellence at the area level, owning the long‑term health, availability, and evolution of critical ML systems, and ensuring robust testing, monitoring, and reliability practices across teams. What We Look For 10+ years of experience researching, designing, deploying, and operating large‑scale, real‑time machine learning systems, with a proven record of driving technical innovation and delivering measurable business impact. Relevant PhD may count for up to 2 YOE. Experience leading end‑to‑end ML system design, from data architecture and feature pipelines to model training, evaluation, and production deployment. Proficient with distributed frameworks such as Spark, Ray, or similar. Strong proficiency in Python and ML frameworks, including PyTorch and XGBoost, and experience with ML tooling for training orchestration, experimentation, and model monitoring such as Kubeflow, MLflow, or equivalent internal platforms. Deep understanding of representation learning and embedding‑based modeling, and expertise in neural network‑based sequence modeling, including Transformers, recurrent, or attention‑based models, and multi‑task learning systems. Hands‑on experience with large‑scale distributed ML infrastructure, including streaming or batch data ingestion, feature stores, feature engineering, training pipelines, model serving and inference infrastructure, monitoring, and automated retraining. Strong technical leadership: defining long‑term strategy, guiding research direction, and aligning work across teams. Recognized as a trusted expert who can drive clarity and execution even in ambiguous problem spaces. Exceptional judgment, collaboration, and communication skills, enabling effective technical discussions with engineers, researchers, and executives. Mentor senior engineers, foster technical excellence, and contribute to a culture of continuous learning. Strong verbal and written communication skills that support effective collaboration across the global engineering organization. Equivalent practical experience or a bachelor’s degree in a related field. Pay & Equity Pay Grade - REquity Grade - 9Employees new to Affirm typically come in at the start of the pay range. The base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100 % subsidized medical coverage, dental and vision for you and your dependents). Employees may also be eligible for equity rewards offered by Affirm Holdings, Inc. .CAN base pay range per year: $206,000 – $256,000 Benefits Health care coverage – Affirm covers all premiums for all levels of coverage for you and your dependents Flexible Spending Wallets – generous stipends for spending on technology, food, various lifestyle needs, and family‑forming expenses Time off – competitive vacation and holiday schedules allowing you to rest and recharge ESPP – An employee stock purchase plan enabling you to buy shares of Affirm at a discount Affirm is proud to be a remote‑first company The majority of roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office‑based due to the nature of their job responsibilities. We want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. (For U.S. positions that could be performed in Los Angeles or San Francisco) Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records. By clicking “Submit Application,” you acknowledge that you have read Affirm 's Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein. #J-18808-Ljbffr